knitr::opts_chunk$set(error=TRUE, comment=NA) library(vetr)
What is Alikeness?
alike is similar to
all.equal from base R except it only compares object
structure. As with
all.equal, the first argument (
target) must be matched
by the second (
library(vetr) alike(integer(5), 1:5) # different values, but same structure alike(integer(5), 1:4) # wrong size alike(integer(26), letters) # same size, but different types
alike only compares structural elements that are defined in
the template). This allows "wildcard" templates. For example, we consider
length zero vectors to have undefined length so those match vectors of any
alike(integer(), 1:5) alike(integer(), 1:4) alike(integer(), letters) # type is still defined and must match
Similarly, if a template does not specify an attribute, objects with any value for that attribute will match:
alike(list(), data.frame()) # a data frame is a list with a attributes alike(data.frame(), list()) # but a list does not have the data.frame attributes
As an extension to the wildcard concept, we interpret partially specified core R attributes. Here we allow any three column integer matrix to match:
mx.tpl <- matrix(integer(), ncol=3) # partially specified matrix alike(mx.tpl, matrix(sample(1:12), nrow=4)) # any number of rows match alike(mx.tpl, matrix(sample(1:12), nrow=3)) # but column count must match
or a data frame of arbitrary number of rows, but same column structure as
iris.tpl <- iris[0, ] # no rows, but structure is defined alike(iris.tpl, iris[1:10, ]) # any number of rows match alike(iris.tpl, CO2) # but column structure must match
"alikeness" is complex to describe, but should be intuitive to grasp. We
recommend you look
example(alike) to get a sense of "alikeness". If you want
to understand the specifics, read on.
alike's template based comparison is declarative. You declare what structure
an object is expected to implement, and
vetr infers all the computations
required to verify that is so. This makes is particularly well suited for
enforcing structural requirements for S3 objects. The S4 system does this and
more, but S3 objects are still used extensively in R code, and sometimes S4
classes are not appropriate.
There are several advantages to template based comparisons:
- Often times it is simpler to define a template than to write out all the checks to confirm an object conforms to a particular structure.
- We can generate the template from a known correct instance of an object and abstract away the elements that are not specific to the prototype (this is particularly valuable for otherwise complex objects).
- We can produce plainish-english interpretations of structural mismatches since we are dealing with a known limited set of comparisons.
The template concept was inspired by
alike compares objects on type,
length, and attributes. Recursive structures are compared
element by element. Language objects and
functions are compared specially because the concept of a value
within those is more complex (e.g., is the
x + y just a value?).
We will defer discussion of attribute comparison to the attributes section.
Objects must be the same length to be
alike, unless the template (
zero length, in which case the object may be any length.
Environments are an exception: we only require that all the
elements present in
target be present in
current. Also, note that calls to
( are ignored in language objects, which may affect
Type comparison is done on type (i.e. the
typeof) with some adjustments to
better align comparisons to "percieved" types as opposed to internal storage
Numerics and Integers
We allow integer vectors to be considered numeric, and short integer-like numerics to be treated as integers:
alike(1L, 1) # `1` is not technically integer, but we treat it as such alike(1L, 1.1) # 1.1 is not integer-like alike(1.1, 1L) # integers can match numerics
This feature is designed to simplify checks for integer-like numbers. The following two expressions are roughly equivalent:
stopifnot(length(x) == 1L && (is.integer(x) || is.numeric(x) && floor(x) == x)) stopifnot(alike(integer(1L), x))
Note that we only check numerics of length <= 100 for
integerness to avoid full scans on large vectors. We expect that the primary
source of these integer-like numerics is hand input vectors (e.g.
c(1, 2, 3)),
so hopefully this compromise is not too limiting. You can modify the threshold
length for this treatment via the
fuzzy.int.max.len parameter to the
settings objects (see
Closures, builtins, and specials are all treated as a single type, even though internally they are stored as different types.
alike will recurse through lists (and by extension data frames), pairlists,
expressions, and environments and will check pairwise alikeness between the
corresponding elements of the
- only the elements present in the template are checked, so
currentmay have additional items
- if the template is the global environment, then
currentmust be too (this is because the global environment is often littered with many objects, and explicitly comparing it to another environment could be computationally expensive)
NULL elements within templates in recursive objects are considered undefined
and as such act like wildcards:
## two NULLs match two length list alike(list(NULL, NULL), list(1:10, letters)) ## but not three length list alike(list(NULL, NULL), list(1:10, letters, iris))
Note that top level
NULLs do not act as wildcards:
alike(NULL, 1:10) # NULL only matches NULL
NULL inconsistently depending on whether it is nested or not is a
compromise designed to make
alike a better fit for argument validation because
arguments that are
NULL by default are fairly common.
alike will check for self-referential loops in nested environments and prevent
infinite recursion. If you somehow introduce a self-referential structure in a
template without using environments then
alike will get stuck in an infinite
We are currently considering adding new comparison modes for lists that would allow for checks more similar to environments (see #29).
Language Objects, Formulas, and Functions
Alikeness for these types of objects is a little harder to define. We have
settled on somewhat arbitrary semantics, though hopefully they are intuitive.
These may change in the future as we gain experience using
alike with these
types of objects. This is particularly true of functions.
Language objects are also compared recursively, but alikeness has a slightly different meaning for them:
alike(quote(sum(a, b)), quote(sum(x, y))) # calls are consistent alike(quote(sum(a, b)), quote(sum(x, x))) # calls are inconsistent alike(quote(mean(a, b)), quote(sum(x, y))) # functions are different
Since variables can contain anything we do not require them to match directly
across calls. In the examples above the second call fails because the template
defines different variables for each argument, but the
current object uses the
same variable twice. The third call fails because the functions are different
and as such the calls are fundamentally different.
If a function is defined in the calling frame,
prior to testing alikeness:
fun <- function(a, b, c) NULL alike(quote(fun(p, q, p)), quote(fun(y, x, x))) # `match.call` re-orders arguments alike(quote(fun(p, q, p)), quote(fun(b=y, x, x)))
Constants match any constants, but keep in mind that expressions like
c(1, 2, 3) are calls to
c respectively, not constants in the context
of language objects.
NULL is a wild card in calls as well:
str(one.arg.tpl <- as.call(list(NULL, NULL))) alike(one.arg.tpl, quote(log(10))) alike(one.arg.tpl, quote(sd(runif(20)))) alike(one.arg.tpl, quote(log(10, 10)))
( are ignored when comparing calls since parentheses are redundant in
call trees because the tree structure encodes operation precedence independent
of operator precedence.
We concede that the rules for "alikeness" of language objects are arbitrary, but hope the outcomes of those rules is generally intuitive. Unfortunately value and structure are somewhat intertwined for language objects so we must impose our own view of what is value and what is structure.
Formulas are treated like calls, except that constants must match:
alike(y ~ x ^ 2, a ~ b ^ 2) alike(y ~ x ^ 2, a ~ b ^ 3)
alike if the signature of the
current function can reasonably
be interpreted as a valid method for the
alike(print, print.default) # print can be the generic for print.default alike(print.default, print) # but not vice versa
A method of a generic must have all arguments present in the generic, with the
same default values if those are defined. If the generic contains
the method may have additional arguments, but must also contain
Potential changes / improvements for function comparison are being considered in #35.
S4 and R5 (RC Objects)
S4 and RC objects are considered alike if
current inherits from
class(target). Since these objects embed structural information in their
alike relies on class alone to establish alikeness.
Objects of the following types are actually references to specific memory locations:
- External Pointers
- Weak References
- Byte codes
These are typically attached as attributes to other objects that contain the
information required to establish alikeness (e.g.
functions), so we only check their type.
Much of the structure of an object is determined by attributes.
recursively compares object attributes and requires them to be
the attribute is a special attribute or an environment.
Environments within attributes in the template must be matched by an
environment, but nothing is checked about the environments to avoid expensive
computations on objects that commonly include environments in their attributes
(e.g. formulas); note this is different than the treatment of environments as
Only attributes present in the template object are checked:
alike(structure(logical(1L), a=integer(3L)), structure(TRUE, a=1:3, b=letters)) alike(structure(TRUE, a=1:3, b=letters), structure(logical(1L), a=integer(3L)))
Attributes present in
current but missing in
target may be anything at all.
The special attributes are
levels. These attributes are discussed in sections 2.2 and 2.3 of
the R Language
and have well defined and consistently applied semantics in R. Since the
semantics of these attributes are well known, we are able to define "alikeness"
for them in a more granular way than we can for arbitrary attributes.
We also consider
srcref to be a special attribute. This attribute is not
row.names and names
If present in
target, then must be matched exactly by the corresponding
current, except that:
- zero length
character(0L)) will match any character
- a zero character element (i.e.
"") in a
row.namescharacter vector will allow any value to match at the corresponding position of the
alike(setNames(integer(), character()), 1:3) alike(setNames(integer(), character()), c(a=1, b=2, c=3)) alike(setNames(integer(3), c("", "", "Z")), c(a=1, b=2, c=3)) alike(setNames(integer(3), c("", "", "Z")), c(a=1, b=2, Z=3))
dim attributes must be identical between
current, except that
if a value of the
dim vector is zero in
target then the corresponding
current can be any value. This is how comparisons like the following
mx.tpl <- matrix(integer(), ncol=3) # partially specified matrix alike(mx.tpl, matrix(sample(1:12), nrow=4)) alike(mx.tpl, matrix(sample(1:12), nrow=3)) # wrong number of columns str(mx.tpl) # notice 0 for 1st dimension
Must also be identical, except that if the
target value of the
for a particular dimension is
NULL, then the corresponding
dimnames value in
current may be anything. As with
names, zero character
elements match any name.
mx.tpl <- matrix(integer(), ncol=3, dimnames=list(row.id=NULL, c("R", "G", ""))) mx.cur <- matrix(sample(0:255, 12), ncol=3, dimnames=list(row.id=1:4, rgb=c("R", "G", "Blue"))) mx.cur2 <- matrix(sample(0:255, 12), ncol=3, dimnames=list(1:4, c("R", "G", "b"))) alike(mx.tpl, mx.cur) alike(mx.tpl, mx.cur2)
dimnames can have a
names attribute. This
names attributed is treated as described in row.names and names.
S3 objects are considered alike if the
current class inherits from the
target class. Note that "inheritance" here is used in a stricter context than in the typical S3 application:
- Every class present in
targetmust be present in
- The overlapping classes must be in the same order
- The last class in
currentmust be the same as the last class in
tpl <- structure(TRUE, class=c("a", "b", "c")) cur <- structure(TRUE, class=c("x", "a", "b", "c")) cur2 <- structure(TRUE, class=c("a", "b", "c", "x")) alike(tpl, cur) alike(tpl, cur2)
tsp attribute of
ts objects behaves similarly to the
dim attribute. Any component (i.e. start, end, frequency) that is set to zero will act as a wild card. Other components must be identical. It is illegal to set
tsp components to zero throught the standard R interface, but you may use
abstract as a work-around.
Levels are compared like row.names and names.
This attribute is completely ignored.
Normal Attributes that Happen To Have Special Names
If an object contains one of the special attributes, but the attribute value is inconsistent with the standard definition of the attribute,
alike will silently treat that attribute as any other normal attribute.
Modifying Comparison Behavior
You can use the
settings parameter to
alike to modify comparison behavior.
?vetr_settings for details.
From The Ground Up
You can always create your own templates by manually building R structures:
int.scalar <- integer(1L) int.mat.2.by.4 <- matrix(integer(), 2, 4) # A df without column names df.chr.num.num <- structure( list(character(), numeric(), numeric()), class="data.frame" )
Abstracting Existing Structures
Alternatively, you can start with a known structure, and abstract away the instance-specific details. For example, suppose we are sending sample collectors out on the field to record information about iris flowers:
iris.tpl <- iris[0, ] alike(iris.tpl, iris.sample.1) # make sure they submit data correctly
iris.tpl <- abstract(iris)
abstract is an S3 generic defined by
alike along with methods for common objects.
abstract primarily sets the
length of atomic vectors to zero:
abstract(list(c(a=1, b=2, c=3), letters))
and also abstracts the
tsp attributes if present. Other attributes are left untouched unless a specific
abstract method exists for a particular object that also modifies attributes. One example of such a method is
abstract.lm, and it does some minor tweaking to the base abstractions to allow us to match models produced by
df.dummy <- data.frame(x=runif(3), y=runif(3), z=runif(3)) mdl.tpl <- abstract(lm(y ~ x + z, df.dummy)) # TRUE, expecting bi-variate model alike(mdl.tpl, lm(Sepal.Length ~ Sepal.Width + Petal.Width, iris)) alike(mdl.tpl, lm(Sepal.Length ~ Sepal.Width, iris))
The error message is telling us that at index
alike was expecting a call to
+ instead of a
Sepal.Width + <somevar> instead of
Sepal.Width). The message
could certainly be more eloquent, but with a little context it should provide
enough information to figure out the problem.
We have gone to great lengths to make
alike fast so that it can be included in
other functions without concerns for what overhead:
type_and_len <- function(a, b) typeof(a) == typeof(b) && length(a) == length(b) # for reference bench_mark(times=1e4, identical(rivers, rivers), alike(rivers, rivers), type_and_len(rivers, rivers) )
alike is slower than
identical and the comparable bare bones R
function, it is competitive with a bare bones R function that checks types and
length. As objects grow more complex,
identical will obviously pull ahead,
alike should be sufficiently fast for most applications:
bench_mark(times=1e4, identical(mtcars, mtcars), alike(mtcars, mtcars) )
In the above example, we are comparing the data frames, their attributes, and the 11 columns individually.
Keep in mind that the complexity of the
alike comparison is driven by the
complexity of the template, not the object we are checking, so we can always
manage the expense of the
Comparisons that succeed will be substantially faster than comparisons that fail as the construction of error messages is non-trivial and we have prioritized optimization in the success case.
Language object comparison is relatively slow. We intend to optimize this some day.
Templates with large numbers of attributes (e.g. > 25) may scale non-linearly. We intend to optimize this some day, though in our experience objects with that many attributes are rare (note having multiple objects each with a handful attributes nested in recursive structures is not a problem).
Large objects will be slower to evaluate. Let us revisit the
though this time we compare our template to itself to ensure that the
comparisons succeed for
mdl.tpl <- abstract(lm(y ~ x + z, data.frame(x=runif(3), y=runif(3), z=runif(3)))) # compare mdl.tpl to itself to ensure success in all three scenarios bench_mark( alike(mdl.tpl, mdl.tpl), all.equal(mdl.tpl, mdl.tpl), # for reference identical(mdl.tpl, mdl.tpl) )
Even with template as large as
lm results (check
str(mdl.tpl)) we can evaluate
alike thousands of times before the overhead becomes noticeable.
Some fairly innocuous R expressions carry substantial overhead. Consider:
df.tpl <- data.frame(a=integer(), b=numeric()) df.cur <- data.frame(a=1:10, b=1:10 + .1) bench_mark( alike(df.tpl, df.cur), alike(data.frame(integer(), numeric()), df.cur) )
data.frame is a particularly slow constructor, but in general you are best
served by defining your templates (including calls to
abstract) outside of
your function so they are created on package load rather than every time your
function is called.
alike as an S3 generic
alike is not currently an S3 generic, but will likely one in the future
provided we can create an implementation with and acceptable performance